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SUMMARY:Sparse Gaussian Process in Disease Mapping - Jarno Vanhatalo\, Hel
 sinki University of Technology
DTSTART:20080122T110000Z
DTEND:20080122T120000Z
UID:TALK10325@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:Disease mapping is a research area in spatial epidemiology\, w
 hich aims to describe the overall disease distribution on a map. The aim m
 ight be\, for example\, to highlight areas of elevated or lowered mortalit
 y or morbidity risk. Gaussian process gives a natural prior for the log ri
 sk surface\, since the spatial correlations between areas can be included 
 in an explicit and natural way into the model via a correlation function. 
 The drawback with using a Gaussian process is the computational burden of 
 the covariance matrix calculations and analytically intractable model. In 
 this talk we consider sparse approximations to Gaussian process prior to s
 peed up the computations and approximate approaches for posterior inferenc
 e. The sparse approximations are fully and partially independent condition
 al (FIC and PIC) and the posterior inference is conducted with a help of M
 arkov chain Monte Carlo methods and expectation propagation.
LOCATION:Engineering Department\, CBL Room 438
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